{"type":"ScholarlyArticle","title":"DART: Navigating Last-Mile Heterogeneity in Instant Delivery via Distribution-Adaptive Splines","authors":[{"name":"Hao Xiong","slug":"hao-xiong","given":"Hao","family":"Xiong","affiliation":""},{"name":"Yang Gao","slug":"yang-gao","given":"Yang","family":"Gao","affiliation":""},{"name":"Haiyong Luo","slug":"haiyong-luo","given":"Haiyong","family":"Luo","affiliation":""},{"name":"Fang Zhao","slug":"fang-zhao","given":"Fang","family":"Zhao","affiliation":""},{"name":"Dan Luo","slug":"dan-luo","given":"Dan","family":"Luo","affiliation":""}],"abstract":"On-demand delivery platforms rely on Travel Time Estimation (TTE) to balance courier earnings and overdue risks. In collaboration with one of China's largest platforms, we address a critical \"Fairness Gap\" in TTE: current systems fail to capture complex delivery patterns in GNSS-denied environments, subjecting couriers handling high concurrent order volumes to disproportionate pressure due to overdue deliveries. Analyzing 1.27 million real-world trajectories, we attribute this bias to unique challenges in GNSS-denied scenarios: distributional heterogeneity, structural heterogeneity, and contextual uncertainty. To bridge this gap, we propose DART (Distribution-Adaptive Robust Timing). DART incorporates a Learnable Adaptive Spline (LAS) encoder with a gradient-driven knot migration mechanism to enhance non-linear expressiveness for outliers, significantly improving long-tail accuracy. Furthermore, a Spatio-Temporal Transition Graph (STTG) reconstructs the latent topology by integrating sequence semantics, such as Wi-Fi-sensed arrival merchant timestamps. At the same time, a Distribution Gating Mechanism characterizes delivery time distributions under distinct contexts. Through extensive experiments and large-scale online A/B testing, DART not only reduces MAE by 14.0% in complex environments but also decreases the Order Overdue Rate by 1.7% (saving $24,000 daily), demonstrating how AI effectively reconciles operational efficiency with labor fairness.","doi":"10.24963/ijcai.2026/836","url":"https://ijcai.org/proceedings/2026/836","pdf":"https://ijcai.org/proceedings/2026/0836.pdf","markdown":"https://ijcai.org/proceedings/2026/836.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/836","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":836,"pages":{"first":7521,"last":7529},"keywords":["Humans and AI: Humans and AI","Machine Learning: Machine Learning","Data Mining: Data Mining"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}